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Control Systems and Optimization Letters
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 118 Documents
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Enhancing Electric Vehicle Performance: A Case Study on Advanced Motor Drive Systems, Integration, Efficiency, and Thermal Management Ali Khan, Md. Yakub
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.152

Abstract

This paper presents a comprehensive review of advanced motor drive systems for next-generation electric vehicles (EVs), focusing on integration, efficiency, thermal management, and sustainability. As the automotive industry transitions towards electrification, the development of efficient motor drive systems is paramount to enhancing vehicle performance and sustainability. This study highlights the integration of various motor technologies, including permanent magnet synchronous motors (PMSMs), induction motors, and switch reluctance motors, with power electronics and thermal management solutions. Key findings reveal that utilizing advanced materials such as silicon carbide (SiC) and gallium nitride (GaN) in power electronics leads to significant improvements in energy efficiency and reduced energy losses. Effective thermal management strategies, including liquid cooling systems and advanced control algorithms, are critical for maintaining optimal operating conditions and enhancing overall system reliability. Furthermore, the paper discusses the sustainability implications of motor drive systems, addressing challenges related to material sourcing and environmental impact while highlighting the importance of recycling initiatives. As the automotive industry transitions towards electrification, the development of efficient motor drive systems becomes crucial for enhancing vehicle performance and environmental sustainability The insights gained from this case study underscore the potential of advanced motor drive systems to shape the future of electric mobility, promoting a more efficient and environmentally friendly transportation landscape. Overall, this research contributes valuable knowledge to the ongoing discourse on the development and implementation of next-generation motor drive technologies in the electric vehicle market.
Enhancing Energy Flexibility: A Case Study on Peer-to-Peer (P2P) Energy Trading Between Electric Vehicles and Microgrid Biswas, Chanchal; Sharma, Anik; Prianka, Yingking Mitra
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.144

Abstract

In order to better understand how Peer-to-Peer (P2P) energy trading between EVs and microgrids might improve energy flexibility, lower costs, and facilitate the integration of renewable energy sources, this case study examines the viability and advantages of this innovative strategy. By allowing EVs to trade energy directly with other EVs or microgrid components, P2P energy trading establishes a decentralized energy market that maximizes the distribution and use of energy. Using real-world situations, this study assesses the technical and economic elements of peer-to-peer (P2P) trading and its effects on user involvement, energy management, and grid stability. By enabling EVs to trade energy directly with one another or with microgrid components, P2P energy trading creates a decentralized energy market that optimizes energy distribution and consumption. The findings demonstrate that P2P trading can greatly lower energy expenses, ease system congestion, and increase energy consumption efficiency overall. P2P trade is a viable option for future energy systems since it guarantees safe and transparent transactions through the use of blockchain technology and smart contracts. Microgrids can adapt to changes in the supply of renewable energy by using P2P technologies. EV batteries, for instance, can store extra solar energy during periods of high production and release it to the grid or other EVs when demand spikes. The results demonstrate how P2P energy trading can help ease the shift to a user-centric, decentralized, and sustainable energy economy.
Understanding Generative Adversarial Networks (GANs): A Review Purwono, Purwono; Wulandari, Annastasya Nabila Elsa; Ma'arif, Alfian; Salah, Wael A.
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.170

Abstract

Generative Adversarial Networks (GANs) is an important breakthrough in artificial intelligence that uses two neural networks, a generator and a discriminator, that work in an adversarial framework. The generator generates synthetic data, while the discriminator evaluates the authenticity of the data. This dynamic interaction forms a minimax game that produces high-quality synthetic data. Since its introduction in 2014 by Ian Goodfellow, GAN has evolved through various innovative architectures, including Vanilla GAN, Conditional GAN (cGAN), Deep Convolutional GAN (DCGAN), CycleGAN, StyleGAN, Wasserstein GAN (WGAN), and BigGAN. Each of these architectures presents a novel approach to address technical challenges such as training stability, data diversification, and result quality. GANs have been widely applied in various sectors. In healthcare, GANs are used to generate synthetic medical images that support diagnostic development without violating patient privacy. In the media and entertainment industry, GANs facilitate the enhancement of image and video resolution, as well as the creation of realistic content. However, the development of GANs faces challenges such as mode collapse, training instability, and inadequate quality evaluation. In addition to technical challenges, GANs raise ethical issues, such as the misuse of the technology for deepfake creation. Legal regulations, detection tools, and public education are important mitigation measures. Future trends suggest that GANs will be increasingly used in text-to-image synthesis, realistic video generation, and integration with multimodal systems to support cross-disciplinary innovation.
An Investigation of the Output Characteristics of Photovoltaic Cells Using Iterative Techniques and MATLAB® 2024a Software Hysa, Azem; Mahmoud, Mohamed Metwally; Ewais, Ahmed
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.174

Abstract

This study investigates the characteristics of photovoltaic (PV) cells using iterative methods and MATLAB® 2024a software. Its main objective is to analyze the power-voltage (P-V) and current-voltage (I-V) characteristics for various series resistances and solar irradiation levels. The precision and dependability of this study are improved by the software utilized for numerical simulations and analysis. Since the PV cells are nonlinear, numerical techniques are favored in this situation to solve their nonlinear equations. In order to investigate different curves and their characteristics, the study makes use of numerical simulations, the single diode model, and the Newton-Raphson method (NRM), which is iterative and converges to an optimal solution of the problem to be solved. The behavior of PV cells under the variation of solar irradiance and different values of series resistance is described by the I-V and P-V characteristics. From the data, we notice that the influence of sun irradiance on PV cells, demonstrates that higher solar irradiance gives more current and power, and higher series resistance decrease the output power. The highest efficiency of a solar cell measured is roughly 47.1%. Future technical advancements in these crucial areas for humankind will result in further increases in the maximum efficiency of solar cells.
Cooperative Intelligent Control of Multi-Agent Systems (MAS) Through Communication, Trust, and Reliability Acha, Stefalo; Yi, Sun
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.171

Abstract

The field of Multi-Agent Systems (MAS) has achieved significant advancements in modern research and development. This study focuses on enhancing trust evaluation, communication efficiency, and adaptive navigation in scenarios where agents have limited prior knowledge. Key contributions include the development of a high-intelligence MAS system that integrates key input data, such as real-time parameters regarding agents’ distances from one another, their distances to target locations, weather conditions, visibility, machine learning capabilities, positions relative to safe or unsafe environments for trust evaluation, delays in communication, and potential cyber threats. These factors trigger a dynamic topology-switching mechanism to secure agents or minimize communication delays in high-security operations. The MAS implements these strategies based on an adaptive communication model, enabling agents to execute various steps during data pooling effectively. Agents utilize real-time data to coordinate flock movements, ensuring dynamic and robust control through data pooling. For example, in a topology requiring a lead agent, the lead agent provides navigation instructions based on pooled data collected during mission execution. This data may involve repositioning proper area coverage, considering agents’ visibility, distance, or environmental disturbances. Four main topologies are implemented in this experiment: directed mesh with two lead agents (type A), directed mesh with one lead agent (type B), star topology (type C), and ring topology (type D). Type B and C topologies are well-suited for communication without delays or disturbances but perform poorly when the system experiences delays (e.g., noise disturbances exceeding a threshold frequency of 5 Hz). In contrast, type A and D topologies are more effective in handling communication delays. By implementing a topology-switching mechanism, this research streamlines the application of two or more topologies in real-life scenarios. It utilizes type B or C topologies in undisturbed conditions and switches to type A or D when perturbations occur. This optimization minimizes communication delays during mission execution and flight time. The research demonstrates significant improvements in trust evaluation, communication efficiency, and overall MAS performance, with implications across various domains, including image and video mining. In these areas, the integration of domain-specific agents enhances processes such as preprocessing, feature extraction, and interpretation. Results show improved accuracy and reliability in data analysis and decision-making across diverse applications, particularly in scenarios involving complex spatial objects and varying environmental conditions.
Audio-Based Telemetry Using HT Radios for Remote Monitoring of Renewable Energy Systems Perkasa, Sigit Dani; Muzadi, Ahmad Rahmad; Megantoro, Prisma; Pandi, Vighneshwaran
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.181

Abstract

Effective monitoring of renewable energy systems, such as wind turbines and photovoltaic arrays, is essential for optimizing energy production. However, traditional wired monitoring systems are expensive and lack flexibility. This study develops a reliable wireless monitoring system that addresses the limitations of wired alternatives, using a PZEM-004T power meter, Arduino Uno R3, and BF-888S HT radios. The system employs audio-modulated binary encoding for long-range, low-cost data transmission, enabling real-time monitoring of key power parameters, including voltage, current, and power factor. This solution offers scalability and cost-effectiveness by eliminating the need for extensive infrastructure. The methodology involves designing both hardware and firmware for the transmitter and receiver components and developing a communication algorithm to optimize data transfer efficiency. The system was tested in various environments: indoor, outdoor, and radio communication scenarios. Performance varied across environments, with outdoor and higher-floor tests experiencing more significant interference, which impacted transmission quality. The system achieved an average transmission time of 42.64 seconds and an error rate of 0.56% across 16 channels, demonstrating competitive reliability compared to existing wireless systems. Future research could explore adaptive modulation techniques to enhance data reliability in high-interference environments, improving the system's robustness for large-scale deployments.
Review of Electrical and Thermal Modeling Techniques for Three-Phase PMSM Drives Azom, Md Ali; Hossain, Md. Shahen; Khan, Md. Yakub Ali
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.172

Abstract

The objective of this paper is to present a thorough examination of electrical and thermal modelling approaches for three-phase PMSM drives, emphasizing their methods, potential, and constraints. Modern electric drives now rely heavily on Permanent Magnet Synchronous Motors (PMSMs), which are found in renewable energy systems, industrial automation, and electric cars. PMSM drives must be accurately modelled to maximize performance, guarantee dependability, and increase operational longevity. The methods, advantages, and disadvantages of electrical and thermal modelling approaches for three-phase PMSMs are thoroughly examined in this paper. To forecast electromagnetic behavior and drive efficiency, the electrical modelling section examines dynamic dq-axis transformations, finite element methods (FEM), equivalent circuit models, and sophisticated AI-driven techniques. The function of thermal modelling tools in controlling heat dissipation and halting thermal degradation is examined. These techniques include lumped parameter models, coupled electro-thermal models, and computational fluid dynamics (CFD). The trade-offs between these models' practical usability, computational complexity, and accuracy are highlighted by a comparative comparison. Incorporating trade-offs between accuracy, complexity, and usability into modelling methods for three-phase Permanent Magnet Synchronous Motor (PMSM) drives offer a comprehensive viewpoint that strikes a compromise between performance and usefulness. Current issues are noted in the review, including the requirement for real-time adaptive models and the incorporation of multi-physics effects. New developments are highlighted as promising paths to improve PMSM modelling, including AI-based simulations and digital twin technologies. The goal of this study is to provide researchers and engineers with a thorough resource that will direct the creation of reliable and effective PMSM drive systems. The review's findings and insights have the potential to influence a variety of applications, spur innovation in PMSM drives, and aid in the global shift to sustainable technologies and electrification.
Recent Developments in Control and Simulation of Permanent Magnet Synchronous Motor Systems Azom, Md Ali; Khan, Md. Yakub Ali
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.173

Abstract

This paper's main goal is to present a thorough analysis of current advancements in the simulation and control of Permanent Magnet Synchronous Motor (PMSM) systems. A crucial part of contemporary electrical drive systems, the Permanent Magnet Synchronous Motor (PMSM) finds extensive use in fields like industrial automation, renewable energy systems, and electric cars. This review examines the most current developments in PMSM system control and simulation, with a focus on cutting-edge modelling techniques, new control strategies, and the most recent simulation methods. It emphasizes how increasingly complex strategies like Model Predictive Control (MPC), Sliding Mode Control (SMC), and AI-based approaches have replaced more conventional ones like PID and vector control. Advanced control techniques like Field-Oriented Control (FOC) and MPC are used by Tesla and other EV manufacturers to maximize PMSM performance, guarantee smooth torque delivery, and improve energy economy. Siemens Gamesa wind turbines use PMSMs with reliable control systems for fault tolerance and maximum energy production in a range of wind conditions. The study also discusses the developments in simulation techniques, such as the incorporation of multi-physics models, real-time simulation, and the application of AI to improve simulation efficiency and accuracy. More realistic modelling of PMSM systems in dynamic contexts is now possible thanks to recent developments in simulation approaches, such as Multiphysics models and real-time simulations. These simulations are combined with sophisticated control algorithms to give real-time input while the system is operating, which speeds up fault finding and optimization. This procedure is further improved by AI-based simulation tools, which forecast system behavior’s under varied circumstances and spot possible problems before they arise. It is described how these advancements affect PMSM performance, including increased fault tolerance, robustness, and efficiency. The study concludes by highlighting the significance of integrating cutting-edge control and simulation approaches for optimal performance in PMSM systems, as well as important research issues and prospects.
Optimized Photoplethysmography-Based Classification of Calf Muscle Fatigue Using Particle Swarm Optimization with Logistic Regression Perkasa, Sigit Dani; Ama, Fadli; Megantoro, Prisma
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.182

Abstract

This study investigates photoplethysmography (PPG) as a non-invasive, cost-effective alternative for real-time muscle fatigue monitoring, addressing limitations inherent to conventional methods like electromyography (EMG) and blood lactate testing. A PPG-based system was developed to classify fatigued versus non-fatigued states of the calf muscle using a DFRobot SEN0203 sensor at a 1000 Hz sampling rate. The raw PPG signals were segmented into 1-second intervals and processed to compute first and second derivatives—yielding vascular (VPG) and arterial (APG) photoplethysmograms—which enabled extraction of key features including heart rate (HR), heart rate variability (HRV), peak systolic and diastolic voltages, maximum systolic slope (u), minimum diastolic slope (v), and arterial stiffness indicators (b–a and c–a ratios). A Particle Swarm Optimization (PSO) algorithm was employed to optimize both feature selection and hyperparameters within a Logistic Regression (LR) model, achieving perfect classification accuracy (1.0) with training and prediction times of 0.0053 s and 0.0016 s, respectively. Notably, HRV and the minimum diastolic slope—reflecting autonomic regulation and vascular compliance—emerged as the most influential features with weights of 12.3747 and 23.9367. Comparative analyses revealed that although LightGBM matched the PSO-LR accuracy, neural network approaches performed poorly (0.50 accuracy), likely due to overfitting and limited training data. These findings underscore the viability of PPG for muscle fatigue monitoring, with promising applications in sports science, rehabilitation, and occupational health.
Review on the Safety and Sustainability of Autonomous Vehicles: Challenges and Future Directions Uzzaman, Asif; Adam, Md Ibrahim; Alam, Shahin; Basak, Pallab
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.185

Abstract

Autonomous vehicles (AVs) represent a major advancement in transportation technology, offering significant benefits such as enhanced road safety, reduced traffic congestion, and improved mobility. However, the widespread deployment of AVs faces key obstacles, including sensor limitations under adverse weather conditions, ethical decision-making in complex scenarios, regulatory challenges, and data privacy concerns. This paper examines these challenges and proposes potential solutions. Key challenges include improving sensor fusion and AI algorithms to enhance perception and decision-making, developing standardized ethical guidelines for autonomous systems, and establishing consistent legal and regulatory standards across regions. Additionally, ensuring cybersecurity and addressing data privacy issues are critical for maintaining the safety and trust of AV users. The future of AVs also depends on advancements in infrastructure, such as the development of smart roads and Vehicle-to-Everything (V2X) communication systems, as well as reducing production costs to increase accessibility. Furthermore, raising public awareness and fostering acceptance through education and transparent communication about AV benefits is vital. The paper concludes that with ongoing research, innovation, and collaboration, AVs have the potential to revolutionize transportation, offering a safer, more efficient, and sustainable future for mobility.

Page 9 of 12 | Total Record : 118

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